Staff Software Engineer (ML) in London

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. We have fleets of hundreds of thousands of nodes across multiple cloud providers and datacenters to train, research, and serve frontier AI models. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow.

Responsibilities:

• Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption.
• Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on.
• Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities.
• Participate in on-call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures.

Qualifications:

• Significant software engineering experience building and operating production distributed systems.
• Proficiency in at least one systems-appropriate language (e.g., Go, Python, Rust, or C++).
• Demonstrated ability to debug complex issues across the stack, from API behaviour down to node and network-level root causes.
• Comfort building consensus with internal stakeholders.
• Experience building or operating cluster schedulers or batch systems (e.g., Background scaling control planes or coordination systems such as etcd, ZooKeeper, Consul, or large DNS/service-mesh deployments).
• Familiarity with ML infrastructure: gang scheduling; collective networking such as NCCL.
• Experience with GCP and/or AWS, including GKE/EKS internals and Infrastructure as Code.
• Low-level systems experience such as Linux kernel tuning, cgroups, or eBPF.
• 8+ years of relevant industry experience, including time leading large, ambiguous infrastructure projects.

Education:

• Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role as demonstrated through coursework, training, or professional experience.

Location-based hybrid policy: Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We think AI systems like the ones we're building have enormous social and ethical implications. We believe that the highest-impact AI research will be big science. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science.

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.